Analytics Vidhya

MAY 24, 2021

ArticleVideo Book This article was published as a part of the Data Science Blogathon INTRODUCTION: Stroke is a medical condition that can lead to the. The post How to create a Stroke Prediction Model? appeared first on Analytics Vidhya.

Predictive Modeling 271

Predictive Modeling Modeling Data Science Publishing 271

Analytics Vidhya

MAY 13, 2022

This article was published as a part of the Data Science Blogathon. Introduction on AutoKeras Automated Machine Learning (AutoML) is a computerised way of determining the best combination of data preparation, model, and hyperparameters for a predictive modelling task.

Predictive Modeling 366

Predictive Modeling Machine Learning Data Science Publishing 366

Analytics Vidhya

DECEMBER 3, 2020

This article was published as a part of the Data Science Blogathon. Introduction The general principle of ensembling is to combine the predictions of various. The post Improve your Predictive Model’s Score using a Stacking Regressor appeared first on Analytics Vidhya.

Predictive Modeling 386

Predictive Modeling Data Science Publishing Modeling 386

Analytics Vidhya

MARCH 22, 2022

This article was published as a part of the Data Science Blogathon. Introduction Feature analysis is an important step in building any predictive model. It helps us in understanding the relationship between dependent and independent variables.

Predictive Modeling 332

Predictive Modeling Data Science Publishing Modeling 332

Analytics Vidhya

DECEMBER 9, 2020

This article was published as a part of the Data Science Blogathon. Introduction While trying to make a better predictive model, we come across. The post Out-of-Bag (OOB) Score in the Random Forest Algorithm appeared first on Analytics Vidhya.

Predictive Modeling 261

Predictive Modeling Data Science Publishing Modeling 261

O'Reilly on Data

MARCH 20, 2019

Apply fair and private models, white-hat and forensic model debugging, and common sense to protect machine learning models from malicious actors. Like many others, I’ve known for some time that machine learning models themselves could pose security risks.

Modeling 225

Modeling Machine Learning Predictive Modeling Consulting 225

Rocket-Powered Data Science

JULY 6, 2021

2) MLOps became the expected norm in machine learning and data science projects. MLOps takes the modeling, algorithms, and data wrangling out of the experimental “one off” phase and moves the best models into deployment and sustained operational phase.

Machine Learning 279

Machine Learning Data-driven Deep Learning IoT 279

O'Reilly on Data

JUNE 18, 2019

Machine learning solutions for data integration, cleaning, and data generation are beginning to emerge. “AI In this post, we shed some light on various efforts toward generating data for machine learning (ML) models. Machine learning applications rely on three main components: models, data, and compute.

Machine Learning 263

Machine Learning Data Quality Statistics Modeling 263

Analytics Vidhya

SEPTEMBER 23, 2021

This article was published as a part of the Data Science Blogathon Overview of Electric Vehicle Sector The supply of fossil fuels is constantly decreasing. The post Data Analysis and Price Prediction of Electric Vehicles appeared first on Analytics Vidhya. The situation is very alarming. A lot of change needs to happen.

Manufacturing 367

Manufacturing Data Science Publishing Analytics 367

Cloudera

FEBRUARY 16, 2022

In this example, the Machine Learning (ML) model struggles to differentiate between a chihuahua and a muffin. In this article, we explore model governance, a function of ML Operations (MLOps). Machine Learning Model Lineage. Machine Learning Model Visibility .

Machine Learning 90

Machine Learning Modeling Metadata Recreation/Entertainment 90

Rocket-Powered Data Science

FEBRUARY 12, 2021

This is accomplished by adding more data-driven, machine learning, and AI (artificial intelligence) components to the process discovery, process mining, and process learning stages. IA incorporates feedback, learning, improvement, and optimization in the automation loop. (AAI) So, what about Intelligent Automation?

ROI 234

ROI Digital Transformation Reporting Machine Learning 234

CIO Business Intelligence

FEBRUARY 10, 2023

Predictive analytics definition Predictive analytics is a category of data analytics aimed at making predictions about future outcomes based on historical data and analytics techniques such as statistical modeling and machine learning. from 2022 to 2028.

Predictive Analytics 145

Predictive Analytics Analytics Predictive Modeling Forecasting 145

O'Reilly on Data

SEPTEMBER 19, 2023

They’d grown tired of learning what is; now they wanted to know what’s next. Stage 2: Machine learning models Hadoop could kind of do ML, thanks to third-party tools. It felt like, almost overnight, all of machine learning took on some kind of neural backend. And it was good.

Machine Learning 253

Machine Learning Testing Modeling Cost-Benefit 253

O'Reilly on Data

MARCH 23, 2021

One is how it gave rise to new forms of information flow: the vision of a novel space in which anybody could publish anything and everyone could find it. On top of this, pre-existing societal biases are being reinforced and promulgated at previously unheard of scales as we increasingly integrate machine learning models into our daily lives.

Internet Publishing and Broadcasting 317

Internet Publishing and Broadcasting Broadcasting Data-driven Interactive 317

CIO Business Intelligence

MAY 26, 2022

Expectedly, advances in artificial intelligence (AI), machine learning (ML), and predictive modeling are giving enterprises – as well as small/medium-sized businesses – a never-before opportunity to automate their recruitment even as they deal with radical changes in workplace practices involving remote and hybrid work.

Advertising 128

Advertising Machine Learning Software Measurement 128

CIO Business Intelligence

MAY 28, 2022

Expectedly, advances in artificial intelligence (AI), machine learning (ML), and predictive modeling are giving enterprises – as well as small/medium-sized businesses – a never-before opportunity to automate their recruitment even as they deal with radical changes in workplace practices involving remote and hybrid work.

Advertising 119

Advertising Machine Learning Software Measurement 119

Data Science and Beyond

JULY 22, 2018

Two years later, I published a post on my then-favourite definition of data science , as the intersection between software engineering and statistics. It’s not all about machine learning. It is now much easier to deploy machine learning models, even without a deep understanding of how they work.

Data Science 79

Data Science Machine Learning Statistics Predictive Modeling 79

Cloudera

NOVEMBER 18, 2020

We are delighted to officially publish this year’s Data Impact Award winners. OVO UnCover enables access to real-time customer data using advanced, intelligent data analytics and machine learning to personalize the customer product interaction experience. Data Impact Achievement Award. Winner: United Overseas Bank.

Internet Publishing and Broadcasting 124

Internet Publishing and Broadcasting Data-driven Broadcasting Digital Transformation 124

Smarten

APRIL 19, 2024

The use of Generative AI, LLM and products such as ChatGPT capabilities has been applied to all kinds of industries, from publishing and research to targeted marketing and healthcare. Nothing…and I DO mean NOTHING…is more prominent in technology buzz today than Artificial Intelligence (AI). billion, with the market growing by 31.1%

Analytics 57

Analytics Predictive Modeling KPI Machine Learning 57

Domino Data Lab

JULY 9, 2019

Wiggins advocated that data scientists find problems that impact the business; re-frame the problem as a machine learning (ML) task; execute on the ML task; and communicate the results back to the business in an impactful way. I still believe that data science is the craft of trying to apply machine learning to some real world problem.

Data Science 112

Data Science Machine Learning Advertising Modeling 112

FineReport

MAY 13, 2020

And, with Tableau Public, published workbooks are “disconnected” from the underlying data sources and require periodic updates when the data changes. It provides data scientists and BI executives with data mining, machine learning, and data visualization capabilities to build effective data pipelines. . From Google.

OLAP 105

OLAP KPI Data mining Business Intelligence 105

Jedox

MAY 17, 2022

Financial planners , Chief Financial Officers, and analysts have all struggled to build accurate methods for predicting what’s likely to happen. Prior to the dawn of advanced statistical analysis and machine learning, predictive analytics efforts fell into 4 broad categories: Guessing , which is the default that most people revert to.

Predictive Analytics 85

Predictive Analytics Finance Analytics Forecasting 85

Ontotext

MAY 25, 2023

The event attracts individuals interested in graph technology, machine learning and natural language processes in numerous verticals, including publishing, government, financial services, manufacturing and retail. Peio called this ecosystem “The Fellowship of the Graph”.

Machine Learning 52

Machine Learning Structured Data Unstructured Data Predictive Modeling 52

Smarten

FEBRUARY 7, 2019

The Gartner report entitled, ‘Augmented Analytics Is the Future of Data and Analytics, published on October 31, 2018, includes the following strategic assumptions: By 2025, a scarcity of data scientists will no longer hinder the adoption of data science and machine learning in organizations.

Optimization 40

Optimization Analytics Predictive Modeling Data-driven 40

AWS Big Data

DECEMBER 11, 2024

Data scientists use notebook environments (such as JupyterLab) to create predictive models for different target segments. First, it can be time consuming for users to learn multiple services development experiences. Data and AI governance Publish your data products to the catalog with glossaries and metadata forms.

Data Lake 91

Data Lake Data Warehouse Data-driven Big Data 91

AWS Big Data

MARCH 7, 2023

This post also discusses the art of the possible with newer innovations in AWS services around streaming, machine learning (ML), data sharing, and serverless capabilities. Also, datasets are accessed for ML, data exporting, and publishing needs. Data outbound Data is often consumed using structured queries for analytical needs.

Analytics 94

Analytics Data Warehouse Data Lake Metadata 94

The Unofficial Google Data Science Blog

DECEMBER 4, 2019

In conferences and research publications, there is a lot of excitement these days about machine learning methods and forecast automation that can scale across many time series. Over the life of the forecast, the data scientist will publish historical accuracy metrics. Forecasting at the “push of a button”?

Forecasting 113

Forecasting Data Science Statistics Uncertainty 113

Jedox

JULY 23, 2019

One of the biggest challenges of automation (Robotic Process Automation) and artificial intelligence/machine learning technologies is our current mindset. AI-based machine learning and predictive analytics will start to give us more powerful crystal balls. Financial Modeling. Crystal ball.

Technology 52

Technology Forecasting Finance Data-driven 52

DataRobot Blog

AUGUST 10, 2022

Snowflake provides a state-of the-art data platform for collating and analysing workforce data, and with the combined addition of DataRobot Solution Accelerator models, trusts can have predictive models running with little experimentation — further accelerated by the wide range of supportive datasets available through the Snowflake Marketplace.

Forecasting 52

Forecasting Data-driven Experimentation Risk 52

Occam's Razor

JANUARY 7, 2019

Learn what Session Quality is in Google Analytics, then learn how to use it in your campaigns to improve conversions. Learn what Smart Bidding is in Google Ads, then learn how to use it in your campaigns to improve outcomes. There is too much goodness in modeling that you are not taking advantage of.

Marketing Analytics 89

Marketing Analytics Marketing Analytics Strategy 89

O'Reilly on Data

SEPTEMBER 11, 2019

Machine learning, artificial intelligence, data engineering, and architecture are driving the data space. The Strata Data Conferences helped chronicle the birth of big data, as well as the emergence of data science, streaming, and machine learning (ML) as disruptive phenomena.

IoT 20

IoT Big Data Data Warehouse Uncertainty 20

Insight

FEBRUARY 19, 2020

Using variability in machine learning predictions as a proxy for risk can help studio executives and producers decide whether or not to green light a film project Photo by Kyle Smith on Unsplash Originally posted on Toward Data Science. Sign up to learn more about the Insight Fellows programs and start your application today.

Risk 67

Risk ROI Modeling Metrics 67

O'Reilly on Data

MAY 18, 2020

And last is the probabilistic nature of statistics and machine learning (ML). Most AI models decay overtime: This phenomenon, known more widely as model decay , refers to the declining quality of AI system results over time, as patterns in new data drift away from patterns learned in training data.

Risk 361

Risk Modeling Data Processing Software 361

Insight

MARCH 18, 2019

Bias in Machine Learning Algorithms (Bottom Photos Source: ProPublica ; Top Photos Source: Pexels.com) Biases in predictive modeling are a widespread issue Machine learning and AI applications are used across industries, from recommendation engines to self-driving cars and more.

Machine Learning 77

Machine Learning Predictive Modeling Modeling Metrics 77

Birst BI

MAY 29, 2018

The mainstream arrival of AI using machine learning automation promises to boost all kinds of software capabilities, particularly BI. Both AI and BI systems use machine learning to make predictions. He published a paper proposing the development of a “relational database model” to address this issue.

Slice and Dice 74

Slice and Dice Analytics Business Intelligence Machine Learning 74

Smarten

JANUARY 24, 2019

What is Automated Machine Learning? Quite simply, it is the means by which your business can optimize resources, encourage collaboration and rapidly and dependably distribute data across the enterprise and use that data to predict, plan and achieve revenue goals. Take for example, the task of performing predictive analytics.

Machine Learning 40

Machine Learning Predictive Modeling Predictive Analytics Forecasting 40

Smarten

OCTOBER 31, 2022

Publishing and delivery agent. Auto Insights and toolsets that leverage machine learning. Out-of-the-Box Mobile BI tools for access from anywhere. Personalized alerts. Real time and cached data management. R Integration for all with no programming experience required. Intuitive, informative reporting. Key Influencer Analysis.

Key Performance Indicator 67

Key Performance Indicator Analytics Cost-Benefit Predictive Modeling 67